xiamuceer
1cde345ed9
2.修复deepseek模型调用问题，舍弃思考过程AI响应内容，只获取结果内容 3.新增会话过期机制，更新后添加到.env中 4.支持用户在生成章节内容时设置字数
464 lines
15 KiB
Python
464 lines
15 KiB
Python
"""
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设置管理 API
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"""
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from fastapi import APIRouter, HTTPException, Request, Depends
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select
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from typing import Dict, Any, List
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from pathlib import Path
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from pydantic import BaseModel
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import httpx
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from app.database import get_db
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from app.models.settings import Settings
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from app.schemas.settings import SettingsCreate, SettingsUpdate, SettingsResponse
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from app.user_manager import User
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from app.logger import get_logger
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from app.config import settings as app_settings, PROJECT_ROOT
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from app.services.ai_service import AIService, create_user_ai_service
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logger = get_logger(__name__)
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router = APIRouter(prefix="/settings", tags=["设置管理"])
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def read_env_defaults() -> Dict[str, Any]:
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"""从.env文件读取默认配置（仅读取，不修改）"""
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return {
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"api_provider": app_settings.default_ai_provider,
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"api_key": app_settings.openai_api_key or app_settings.anthropic_api_key or "",
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"api_base_url": app_settings.openai_base_url or app_settings.anthropic_base_url or "",
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"model_name": app_settings.default_model,
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"temperature": app_settings.default_temperature,
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"max_tokens": app_settings.default_max_tokens,
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}
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def require_login(request: Request):
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"""依赖：要求用户已登录"""
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if not hasattr(request.state, "user") or not request.state.user:
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raise HTTPException(status_code=401, detail="需要登录")
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return request.state.user
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async def get_user_ai_service(
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user: User = Depends(require_login),
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db: AsyncSession = Depends(get_db)
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) -> AIService:
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"""
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依赖：获取当前用户的AI服务实例
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从数据库读取用户设置并创建对应的AI服务
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"""
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result = await db.execute(
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select(Settings).where(Settings.user_id == user.user_id)
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)
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settings = result.scalar_one_or_none()
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if not settings:
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# 如果用户没有设置，从.env读取并保存
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env_defaults = read_env_defaults()
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settings = Settings(
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user_id=user.user_id,
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**env_defaults
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)
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db.add(settings)
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await db.commit()
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await db.refresh(settings)
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logger.info(f"用户 {user.user_id} 首次使用AI服务，已从.env同步设置到数据库")
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# 使用用户设置创建AI服务实例
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return create_user_ai_service(
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api_provider=settings.api_provider,
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api_key=settings.api_key,
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api_base_url=settings.api_base_url or "",
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model_name=settings.model_name,
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temperature=settings.temperature,
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max_tokens=settings.max_tokens
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)
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@router.get("", response_model=SettingsResponse)
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async def get_settings(
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user: User = Depends(require_login),
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db: AsyncSession = Depends(get_db)
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):
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"""
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获取当前用户的设置
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如果用户没有保存过设置，自动从.env创建并保存到数据库
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"""
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result = await db.execute(
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select(Settings).where(Settings.user_id == user.user_id)
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)
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settings = result.scalar_one_or_none()
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if not settings:
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# 如果用户没有保存过设置，从.env读取默认配置并保存到数据库
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env_defaults = read_env_defaults()
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logger.info(f"用户 {user.user_id} 首次获取设置，自动从.env同步到数据库")
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# 创建新设置并保存到数据库
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settings = Settings(
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user_id=user.user_id,
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**env_defaults
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)
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db.add(settings)
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await db.commit()
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await db.refresh(settings)
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logger.info(f"用户 {user.user_id} 的设置已从.env同步到数据库")
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logger.info(f"用户 {user.user_id} 获取已保存的设置")
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return settings
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@router.post("", response_model=SettingsResponse)
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async def save_settings(
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data: SettingsCreate,
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user: User = Depends(require_login),
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db: AsyncSession = Depends(get_db)
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):
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"""
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创建或更新当前用户的设置（Upsert）
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如果设置已存在则更新，否则创建新设置
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仅保存到数据库
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"""
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# 查找现有设置
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result = await db.execute(
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select(Settings).where(Settings.user_id == user.user_id)
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)
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settings = result.scalar_one_or_none()
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# 准备数据
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settings_dict = data.model_dump(exclude_unset=True)
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if settings:
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# 更新现有设置
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for key, value in settings_dict.items():
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setattr(settings, key, value)
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await db.commit()
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await db.refresh(settings)
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logger.info(f"用户 {user.user_id} 更新设置")
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else:
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# 创建新设置
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settings = Settings(
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user_id=user.user_id,
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**settings_dict
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)
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db.add(settings)
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await db.commit()
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await db.refresh(settings)
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logger.info(f"用户 {user.user_id} 创建设置")
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return settings
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@router.put("", response_model=SettingsResponse)
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async def update_settings(
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data: SettingsUpdate,
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user: User = Depends(require_login),
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db: AsyncSession = Depends(get_db)
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):
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"""
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更新当前用户的设置
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仅保存到数据库
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"""
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result = await db.execute(
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select(Settings).where(Settings.user_id == user.user_id)
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)
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settings = result.scalar_one_or_none()
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if not settings:
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raise HTTPException(status_code=404, detail="设置不存在，请先创建设置")
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# 更新设置
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update_data = data.model_dump(exclude_unset=True)
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for key, value in update_data.items():
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setattr(settings, key, value)
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await db.commit()
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await db.refresh(settings)
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logger.info(f"用户 {user.user_id} 更新设置")
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return settings
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@router.delete("")
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async def delete_settings(
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user: User = Depends(require_login),
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db: AsyncSession = Depends(get_db)
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):
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"""
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删除当前用户的设置
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"""
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result = await db.execute(
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select(Settings).where(Settings.user_id == user.user_id)
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)
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settings = result.scalar_one_or_none()
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if not settings:
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raise HTTPException(status_code=404, detail="设置不存在")
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await db.delete(settings)
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await db.commit()
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logger.info(f"用户 {user.user_id} 删除设置")
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return {"message": "设置已删除", "user_id": user.user_id}
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@router.get("/models")
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async def get_available_models(
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api_key: str,
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api_base_url: str,
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provider: str = "openai"
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):
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"""
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从配置的 API 获取可用的模型列表
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Args:
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api_key: API 密钥
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api_base_url: API 基础 URL
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provider: API 提供商 (openai, anthropic, azure, custom)
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Returns:
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模型列表
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"""
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try:
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async with httpx.AsyncClient(timeout=10.0) as client:
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if provider == "openai" or provider == "azure" or provider == "custom":
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# OpenAI 兼容接口获取模型列表
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url = f"{api_base_url.rstrip('/')}/models"
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json"
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}
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logger.info(f"正在从 {url} 获取模型列表")
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response = await client.get(url, headers=headers)
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response.raise_for_status()
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data = response.json()
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models = []
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if "data" in data and isinstance(data["data"], list):
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for model in data["data"]:
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model_id = model.get("id", "")
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# 过滤出常用的文本生成模型
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if any(keyword in model_id.lower() for keyword in [
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"gpt", "gemini", "claude", "llama", "mistral", "qwen", "deepseek"
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]):
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models.append({
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"value": model_id,
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"label": model_id,
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"description": model.get("description", "") or f"Created: {model.get('created', 'N/A')}"
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})
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if not models:
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raise HTTPException(
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status_code=404,
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detail="未能从 API 获取到可用的模型列表"
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)
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logger.info(f"成功获取 {len(models)} 个模型")
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return {
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"provider": provider,
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"models": models,
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"count": len(models)
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}
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elif provider == "anthropic":
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# Anthropic 没有公开的模型列表API
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raise HTTPException(
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status_code=400,
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detail="Anthropic 不支持自动获取模型列表，请手动输入模型名称"
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)
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else:
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raise HTTPException(
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status_code=400,
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detail=f"不支持的提供商: {provider}"
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)
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except httpx.HTTPStatusError as e:
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logger.error(f"获取模型列表失败 (HTTP {e.response.status_code}): {e.response.text}")
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raise HTTPException(
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status_code=400,
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detail=f"无法从 API 获取模型列表 (HTTP {e.response.status_code})"
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)
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except httpx.RequestError as e:
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logger.error(f"请求模型列表失败: {str(e)}")
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raise HTTPException(
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status_code=400,
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detail=f"无法连接到 API: {str(e)}"
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)
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"获取模型列表时发生错误: {str(e)}")
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raise HTTPException(
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status_code=500,
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detail=f"获取模型列表失败: {str(e)}"
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)
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class ApiTestRequest(BaseModel):
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"""API 测试请求模型"""
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api_key: str
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api_base_url: str
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provider: str
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model_name: str
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@router.post("/test")
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async def test_api_connection(data: ApiTestRequest):
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"""
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测试 API 连接和配置是否正确
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Args:
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data: 包含 API 配置的请求数据
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Returns:
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测试结果包含状态、响应时间和详细信息
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"""
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api_key = data.api_key
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api_base_url = data.api_base_url
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provider = data.provider
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model_name = data.model_name
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import time
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try:
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start_time = time.time()
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# 创建临时 AI 服务实例
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test_service = AIService(
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api_provider=provider,
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api_key=api_key,
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api_base_url=api_base_url,
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default_model=model_name,
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default_temperature=0.7,
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default_max_tokens=100
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)
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# 发送简单的测试请求
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test_prompt = "请用一句话回复：测试成功"
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logger.info(f"🧪 开始测试 API 连接")
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logger.info(f" - 提供商: {provider}")
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logger.info(f" - 模型: {model_name}")
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logger.info(f" - Base URL: {api_base_url}")
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response = await test_service.generate_text(
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prompt=test_prompt,
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provider=provider,
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model=model_name,
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temperature=0.7,
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max_tokens=8000
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)
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end_time = time.time()
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response_time = round((end_time - start_time) * 1000, 2) # 转换为毫秒
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logger.info(f"✅ API 测试成功")
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logger.info(f" - 响应时间: {response_time}ms")
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logger.info(f" - 响应内容: {response[:100] if response else 'N/A'}")
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return {
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"success": True,
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"message": "API 连接测试成功",
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"response_time_ms": response_time,
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"provider": provider,
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"model": model_name,
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"response_preview": response[:100] if response and len(response) > 100 else response,
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"details": {
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"api_available": True,
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"model_accessible": True,
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"response_valid": bool(response)
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}
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}
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except ValueError as e:
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# 配置错误
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error_msg = str(e)
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logger.error(f"❌ API 配置错误: {error_msg}")
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return {
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"success": False,
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"message": "API 配置错误",
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"error": error_msg,
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"error_type": "ConfigurationError",
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"suggestions": [
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"请检查 API Key 是否正确",
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"请确认 API Base URL 格式正确",
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"请验证所选提供商是否匹配"
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]
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}
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except TimeoutError as e:
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# 超时错误
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error_msg = str(e)
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logger.error(f"❌ API 请求超时: {error_msg}")
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return {
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"success": False,
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"message": "API 请求超时",
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"error": error_msg,
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"error_type": "TimeoutError",
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"suggestions": [
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"请检查网络连接",
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"请确认 API Base URL 是否可访问",
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"如果使用代理，请检查代理设置"
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]
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}
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except Exception as e:
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# 其他错误
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error_msg = str(e)
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error_type = type(e).__name__
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logger.error(f"❌ API 测试失败: {error_msg}")
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logger.error(f" - 错误类型: {error_type}")
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# 分析错误原因并提供建议
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suggestions = []
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if "blocked" in error_msg.lower():
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suggestions = [
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"请求被 API 提供商阻止",
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"可能原因：API Key 被限制或地区限制",
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"建议：检查 API Key 状态和账户余额",
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"建议：尝试更换 API Base URL 或使用代理"
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]
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elif "unauthorized" in error_msg.lower() or "401" in error_msg:
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suggestions = [
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"API Key 认证失败",
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"建议：检查 API Key 是否正确",
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"建议：确认 API Key 是否过期"
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]
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elif "not found" in error_msg.lower() or "404" in error_msg:
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suggestions = [
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"API 端点不存在或模型不可用",
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"建议：检查 API Base URL 是否正确",
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"建议：确认模型名称是否正确"
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]
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elif "rate limit" in error_msg.lower() or "429" in error_msg:
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suggestions = [
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"API 请求频率超限",
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"建议：稍后重试",
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"建议：升级 API 套餐"
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]
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elif "insufficient" in error_msg.lower() or "quota" in error_msg.lower():
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suggestions = [
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"API 配额不足",
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"建议：检查账户余额",
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"建议：充值或升级套餐"
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]
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else:
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suggestions = [
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"请检查所有配置参数是否正确",
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"请确认网络连接正常",
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"请查看详细错误信息"
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]
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return {
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"success": False,
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"message": "API 测试失败",
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"error": error_msg,
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"error_type": error_type,
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"suggestions": suggestions
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} |